Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
Wavelet auto-encoder is designed with wavelet function to capture the signal characteristics.A deep wavelet auto-encoder is constructed with multiple wavelet auto-encoders to enhance the unsupervised feature learning ability.The proposed method ...
Generating machine-executable plans from end-user's natural-language instructions
A task-centered semantic analysis method is developed.A machine-execution-specification method is developed. It is critical for advanced manufacturing machines to autonomously execute a task by following an end-user's natural language (NL) instructions. ...
CNC-NOS
A novel class noise cleaner able not only to remove noisy instances but to successfully relabel them.It introduces an improvement of the noise score measure already proposed in a recent filter, making it sensitive to the proximity of the neighbors.A ...
Indicator and reference points co-guided evolutionary algorithm for many-objective optimization problems
A new algorithm IREA is proposed through creatively combining indicator +I with reference points. IREA takes advantage of strength of indicator +I and reference points on the convergence and diversity respectively.In order to produce better offspring, a ...
Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition
QoS-aware Web service composition is regarded as one of the fundamental issues in service computing. Given the open and dynamic internet environment, which lacks a central control of individual service providers, we propose in this paper a novel method ...
Venture capital group decision-making with interaction under probabilistic linguistic environment
As the tide of mass entrepreneurship and innovation sweeps across China, venture capital (VC) is becoming increasingly prominent in economic field and has attracted the attention of the public. The experience of the developed countries shows that VC ...
Centrality measure in social networks based on linear threshold model
Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social ...
Differential evolution for filter feature selection based on information theory and feature ranking
Feature selection is an essential step in various tasks, where filter feature selection algorithms are increasingly attractive due to their simplicity and fast speed. A common filter is to use mutual information to estimate the relationships between ...
Improving user recommendation by extracting social topics and interest topics of users in uni-directional social networks
With the rapid growth of population on social networks, people are confronted with information overload problem. This clearly makes filtering the targeted users a demanding and key research task. Uni-directional social networks are the scenarios where ...
An approach to EEG-based gender recognition using entropy measurement methods
It was possible to use EEG signals for gender recognition. The highest recognition rate in this study was up to 0.998 based on a combination of FE feature and vote method, which could meet the needs of daily applications.The effect of the bagging ...
Discernibility matrix based incremental attribute reduction for dynamic data
Dynamic data, in which the values of objects vary over time, are ubiquitous in real applications. Although researchers have developed a few incremental attribute reduction algorithms to process dynamic data, the reducts obtained by these algorithms are ...
A binary-continuous invasive weed optimization algorithm for a vendor selection problem
Integrating vendor selection, store allocation, and inventory-related decisions.Considering multi-sourcing strategy on vendor selection problem.Developing a novel meta-heuristic solution algorithm (BCIWO).Utilizing GAMS/BARON for performance ...
Characterizing context-aware recommender systems
Context-aware recommender systems leverage the value of recommendations by exploiting context information that affects user preferences and situations, with the goal of recommending items that are really relevant to changing user needs. Despite the ...
Knowledge discovery of consensus and conflict interval-based temporal patterns
Temporal pattern mining problems, developed from sequential pattern mining problems, have recently been discussed frequently regarding the gathering of temporal sequences and aggregating them in order to gain insight into consensus decision-making. ...